A self-tuning load frequency control strategy for microgrids: Human brain emotional learning

Khalghani, Mohammad Reza; Khooban, Mohammad Hassan; Mahboubi-Moghaddam, Esmaeil; Vafamand, Navid; Goodarzi, Mohammad

doi:10.1016/j.ijepes.2015.08.026

Cited by 108 publications

(50 citation statements)

References 16 publications

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“…This problem is more complicated by increasing the number of the MFs. Evolutionary algorithms are good tools for tuning these parameters; therefore, by using the computational ability of the computers, these complicated problems are solvable [43][44][45][46][47][48]. To tackle this problem, we introduced particle swarm optimization (PSO) algorithm to tune these MFs.…”

Section: Tuning Fuzzy Membership Functionsmentioning

confidence: 99%

Robust Nonlinear Fuzzy Formation Control of Unmanned Quadrotors

Khakshour¹,

Nourani²

2017

IJCA

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A novel leader-following strategy based on fuzzy logic is introduced to design a formation flight controller for unmanned quadrotors. The proposed strategy uses particle swarm optimization (PSO) to optimize the fuzzy membership function in the guidance law, and a nonlinear dynamic inversion (NDI) controller is designed to control the nonlinear dynamics of the quadrotor. The simulation results show the proposed method has significant advantages in comparison with conventional leading-following strategies in terms of robustness against wind gusts, uncertainties, and unknown dynamics.

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Section: Tuning Fuzzy Membership Functionsmentioning

confidence: 99%

Robust Nonlinear Fuzzy Formation Control of Unmanned Quadrotors

Khakshour¹,

Nourani²

2017

IJCA

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“…Most recently, meta-heuristic-based optimization algorithms inspired by biological phenomena and natural occurrences have been proven to perform much better in comparison with other widely available algorithms [32][33][34]. One immensely useful addition to this arsenal is the imperialistic competitive algorithm (ICA), which does not require the function gradient (or slope) during its optimization process.…”

Section: Imperialist Competitive Algorithmmentioning

confidence: 99%

Optimal Operation of Combined Photovoltaic Electrolyzer Systems

2016

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Abstract:In this study, the design and simulation of a combination of a photovoltaic (PV) array with an alkaline electrolyzer is performed to maximize the production of hydrogen as a reliable power resource. Detailed electrical model of PV system, as long as thermal and electrochemical model of electrolyzer is used. Since an electrolyzer is a non-linear load, its coupling with PV systems to get the best power transfer is very important. Solar irradiation calculations were done for the region of Miami (FL, USA), giving an optimal surface slope of 25.7˝for the PV array. The size of the PV array is optimized, considering maximum hydrogen production and minimum excess power production in a diurnal operation of a system using the imperialistic competitive algorithm (ICA). The results show that for a 10 kW alkaline electrolyzer, a PV array with a nominal power of 12.3 kW The results show that 12.3 kW photvoltaic system can be utilized for supplying a 10 kW electrolyzer. Hydrogen production and Faraday efficiency of the system are 697.21 mol and 0.3905 mol, respectively.

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“…The cuckoo search algorithm (CSA) and intelligent bee colony optimization (IBCO) are just two of the many examples of these methods [6,9,10]. Khalghani et al compared an emotional controller with the conventional PID controller [11]. A sugeno fuzzy logic controller (S-FLC), optimized by particle swarm optimisation (PSO), was applied by Sangawong et al [12].…”

Section: Introductionmentioning

confidence: 99%

“…A sugeno fuzzy logic controller (S-FLC), optimized by particle swarm optimisation (PSO), was applied by Sangawong et al [12]. For micro grid networks with photovoltaic (PV) systems, battery units, micro wind turbines (MWT), and fuel cells, the most suitable control method is a self-adjusting control method [11,13,14]. Hence, Shankar et al realized a genetic algorithm (GA) for LFC in a two area interconnected hydro-thermal power system [15].…”

Section: Introductionmentioning

confidence: 99%

Use of the Genetic Algorithm-Based Fuzzy Logic Controller for Load-Frequency Control in a Two Area Interconnected Power System

2017

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Abstract:The use of renewable energy resources has created some problems for power systems. One of the most important of these is load frequency control (LFC). In this study, in order to solve the LFC problem, modern control methods were applied to a two area multi source interconnected power system. A photovoltaic solar power plant (PV-SPP) was also connected, in order to identify the harmful effects on the frequency of the system. A new Genetic-based Fuzzy Logic (GA-FL) controller was designed to control the frequency of the system. For comparison, conventional proportional-integral-derivative (PID), fuzzy logic (FL), and Genetic Algorithm (GA)-PID controllers were also designed. The new control method exhibited a better performance than the conventional and other modern control methods, because of the low overshoot and short settling time. All simulations were realized with the Matlab-Simulink program.

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A self-tuning load frequency control strategy for microgrids: Human brain emotional learning

Cited by 108 publications

References 16 publications

Robust Nonlinear Fuzzy Formation Control of Unmanned Quadrotors

Robust Nonlinear Fuzzy Formation Control of Unmanned Quadrotors

Optimal Operation of Combined Photovoltaic Electrolyzer Systems

Use of the Genetic Algorithm-Based Fuzzy Logic Controller for Load-Frequency Control in a Two Area Interconnected Power System

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